code: def test_save_load_pretrained_additional_features(self):
      processor = SeamlessM4TProcessor(
          tokenizer=self.get_tokenizer(),
          feature_extractor=self.get_feature_extractor(),
      )
      processor.save_pretrained(self.tmpdirname)
      tokenizer_add_kwargs = self.get_tokenizer(
          bos_token="(BOS)", eos_token="(EOS)"
      )
      feature_extractor_add_kwargs = self.get_feature_extractor(
          do_normalize=False, padding_value=1.0
      )
  
      processor = SeamlessM4TProcessor.from_pretrained(
          self.tmpdirname,
          bos_token="(BOS)",
          eos_token="(EOS)",
          do_normalize=False,
          padding_value=1.0,
      )
  
      self.assertEqual(
          processor.feature_extractor.to_json_string(),
          feature_extractor_add_kwargs.to_json_string(),
      )
      self.assertIsInstance(
          processor.feature_extractor, SeamlessM4TFeatureExtractor
      )
      self.assertEqual(
          processor.tokenizer.get_vocab(),
          tokenizer_add_kwargs.get_vocab(),
      )
  
      tokenizer_instance = isinstance(
          processor.tokenizer, SeamlessM4TTokenizerFast
      ) or isinstance(processor.tokenizer, SeamlessM4TTokenizer)
      self.assertTrue(tokenizer_instance)
  
file_path: transformers/tests/models/seamless_m4t/test_processor_seamless_m4t.py
imports: import shutil
  import tempfile
  import unittest
  from transformers import SeamlessM4TFeatureExtractor, SeamlessM4TProcessor
  from transformers.models.seamless_m4t import SeamlessM4TTokenizer, SeamlessM4TTokenizerFast
  from transformers.testing_utils import require_torch
  from .test_feature_extraction_seamless_m4t import floats_list
label_desc: Use image node_id 6 for calling the SeamlessM4TProcessorTest obj's underlying member method code with example usage: obj.test_save_load_pretrained_additional_features() without return types